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arules: Mining Association Rules using R

R is a free software environment for statistical computing and graphics widely used for data mining. Association rule mining (see research page on association rules) is one of the most successful data mining techniques. The R add-on package arules implements the basic infrastructure for creating and manipulating transaction databases and basic algorithms to efficiently find and analyze association rules. Several more packages provide provide additional functionality like frequent sequence mining, association rule visualization and associative classification techniques. Compared to other tools, the arules framework is fully integrated, implements the latest approaches and has the vast functionality of R for further analysis of found patterns at its disposal.

Team

Developed Software

Publications

  1. Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977-1981, 2011.
  2. Michael Hahsler and Sudheer Chelluboina. Visualizing association rules in hierarchical groups. In 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms (Interface 2011). The Interface Foundation of North America, 2011.
  3. Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association rule generation. Computational Statistics, 23(2):303-315, April 2008.
  4. Michael Hahsler and Kurt Hornik. New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437-455, 2007.
  5. Michael Hahsler and Kurt Hornik. Building on the arules infrastructure for analyzing transaction data with R. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V., Freie Universität Berlin, March 8-10, 2006, Studies in Classification, Data Analysis, and Knowledge Organization, pages 449-456. Springer-Verlag, 2007.
  6. Thomas Reutterer, Michael Hahsler, and Kurt Hornik. Data Mining und Marketing am Beispiel der explorativen Warenkorbanalyse. Marketing ZFP, 29(3):165-181, 2007.
  7. Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2):137-166, September 2006.
  8. Michael Hahsler, Kurt Hornik, and Thomas Reutterer. Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Magdeburg, March 9-11, 2005, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598-605. Springer-Verlag, 2006.
  9. Michael Hahsler, Bettina Grün, and Kurt Hornik. arules - A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1-25, October 2005.
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